Estimating the Recreation Demand and Economic Value of Mountain Biking in Moab,Utah: An Application of Count Data Models

This paper reports the results of both standard and truncated count data travel cost demand models for estimating demand for and the economic value to participants in mountain biking in the Moab, Utah, area. The empirical estimates for average trip demand per person per season were 2.25 and 2.53 trips under truncated Poisson and truncated negative binomial models, respectively. Consumer surplus per person per trip for both models was approximately US $585. The total annual use value for mountain biking in the Moab area was US $1.33 million. This value suggests that this recreation has a higher value than most other activities in the area and that public land managers should be aware of the relative value of mountain biking as they make allocation decisions.     

GENERATION OF DAILY PRECIPITATION OVER AN AREA1

ABSTRACT: A model was developed of the periodic-stochastic structure of daily precipitation over an area. The model is based ona multivariate normal distribution. The square roots of daily precipitation at a point were found to approximate a sample from a univariate normal distribution that had been truncated at zero. The zero daily precipitation amounts were considered negative amounts of unknown quantity. The multivariate normal distribution was used to describe the variation of daily precipitation over an area. The periodic fluctuations of the model parameters were described with Fourier series. The model was tested using data from two areas of different precipitation characteristics. Data generated with the model contained many of the statistical characteristics observed in the historical data.     

Measuring the Social Recreation Per-Day Net Benefit of the Wildlife Amenities of a National Park: A Count-Data Travel-Cost Approach

In this article, we apply count-data travel-cost methods to a truncated sample of visitors to estimate the Peneda-Gerês National Park (PGNP) average consumer surplus (CS) for each day of visit. The measurement of recreation demand is highly specific because it is calculated by number of days of stay per visit. We therefore propose the application of altered truncated count-data models or truncated count-data models on grouped data to estimate a single, on-site individual recreation demand function, with the price (cost) of each recreation day per trip equal to out-of-pocket and time travel plus out-of-pocket and on-site time costs. We further check the sensitivity of coefficient estimations to alternative models and analyse the welfare measure precision by using the delta and simulation methods by Creel and Loomis. With simulated limits, CS is estimated to be €194 (range €116 to €448). This information is of use in the quest to improve government policy and PNPG management and conservation as well as promote nature-based tourism. To our knowledge, this is the first attempt to measure the average recreation net benefits of each day of stay generated by a national park by using truncated altered and truncated grouped count-data travel-cost models based on observing the individual number of days of stay.     

MODELING TIDAL HYDRODYNAMICS OF SAN DIEGO BAY,CALIFORNIA1

ABSTRACT: In 1983, current data were collected by the National Oceanic and Atmospheric Administration using mechanical current meters. During 1992 through 1996, acoustic Doppler current profilers as well as mechanical current meters and tide gauges were used. These measurements not only document tides and tidal currents in San Diego Bay, but also provide independent data sets for model calibration and verification. A high resolution (100-m grid), depth-averaged, numerical hydrodynamic model has been implemented for San Diego Bay to describe essential tidal hydrodynamic processes in the bay. The model is calibrated using the 1983 data set and verified using the more recent 1992–1996 data. Discrepancies between model predictions and field data in both model calibration and verification are on the order of the magnitude of uncertainties in the field data. The calibrated and verified numerical model has been used to quantify residence time and dilution and flushing of contaminant effluent into San Diego Bay. Furthermore, the numerical model has become an important research tool in ongoing hydrodynamic and water quality studies and in guiding future field data collection programs.     

The contingent behavior of charter fishing participants on the Chesapeake Bay: Welfare estimates associated with water quality improvements

Water quality in the Chesapeake Bay has deteriorated over recent years. Historically, fishing has contributed to the region's local economy in terms of commercial and recreational harvests. A contingent behavior model is used to estimate welfare measures for charter fishing participants with regard to a hypothetical improvement in water quality. Using a truncated Poisson count model corrected for endogenous stratification, it was found that charter fishers not only contribute to the local market economy, but they also place positive non-market value on preserving the Bay's water quality. Using two estimates for travels costs it is estimated that the individual consumer surplus is $200 and $117 per trip, and the average individual consumer surplus values for an improvement in water quality is $75 and $44 for two models estimated.     

AN EXPANSION OF ROGER BARKER'S BEHAVIOR SETTING SURVEY FOR AN ETHNO-ECOLOGICAL APPROACH TO PERSON–ENVIRONMENT INTERACTIONS

Roger Barker's Behavior Setting Survey is generally recognized as the first major effort to bring physical and social contexts into psychological methodologies for studying human behavior. Since Barker's day, advances in social theory, speech act philosophy, pragmatics and other disciplines within the human sciences have converged with Barker's concerns in various ways, making it possible to sharpen and enrich a number of his formulations. The methodological school of critical qualitative research in many ways occupies the vanguard of approaches synthesizing advances in social theory for methodological application. Critical qualitative research, however, has yet to take advantage of Barker's work on behavior settings. Thus both Barker's work and the work of critical ethnographers can be enriched through a synthesis of their theoretical constructs and procedures. This pilot study explores the advantages of synthesizing behavior setting survey and critical qualitative methodology. A university department was studied through a truncated version of the Behavior Setting Survey (BSS) and also with Carspecken's five-stage model for critical social research. Results are significant on both substantive and analytical levels. It was found that departmental behavior patterns discovered through the BSS are only explicable when their cultural conditions are reconstructed via critical qualitative data analysis. This was true particularly with respect to the ‘circuitry’ discovered by the BSS but explained through cultural reconstructions.     

The role of remote sensing in hydrological modelling of the Okavango Delta, Botswana

A coupled surface water-groundwater model of the Okavango Delta has been built based on the United States Geological Survey software MODFLOW 2000 including the SFR2 package for stream-flow routing. It will provide a new tool for evaluating water management and climate change scenarios. The delta's size and limited accessibility make direct, on the ground data acquisition difficult. Remote sensing methods are the most promising source of acquiring spatially distributed data for both model input parameters and calibration. Topography, aquifer thickness, channel positions, evapotranspiration and precipitation data are all based on remote sensing. Simulated flooding patterns are compared to patterns derived from visible to thermal NOAA-AVHRR data and microwave radar ENVISAT-ASAR data.     

Parameter Uncertainty Reduction for SWAT Using Grace,Streamflow, and Groundwater Table Data for Lower Missouri River Basin1

Abstract: This study incorporates the newly available Gravity Recovery and Climate Experiment (GRACE) water storage data and water table data from well logs to reduce parameter uncertainty in Soil and Water Assessment Tool (SWAT) calibration using a SUFI2 (sequential uncertainty fitting) framework for the Lower Missouri River Basin. Model evaluations are performed in multiple stages using a multiobjective function consisting of multisite streamflow and GRACE water storage data as well as a groundwater component. Results show that (1) a model calibrated with both streamflow and GRACE data simultaneously can maintain the water balance for the whole basin, but may improperly partition surface flow and base flow. Additional inclusion of the groundwater constraint can significantly improve the model performance in groundwater hydrological processes. In our case, the estimation of specific yield of shallow aquifers has been increased to 10^?2 from previous much underestimated level (<10^?3). (2) The daily streamflow data are needed to confine the parameters related to water flow in channels such as the Manning’s coefficient, which are less sensitive to the monthly simulations. (3) Parameters are nonuniformly sensitive for different goal variables, and thus, proper specification of a prior distribution of parameters may be the key factor for global optimization algorithms to obtain stable and realistic model performance.     

BUILDUP,WASHOFF, AND EVENT MEAN CONCENTRATIONS1

ABSTRACT: Most watershed water quality simulation models require the user to specify pollutant buildup and washoff rate parameters for pollutants, by land use. Buildup and washoff rates are difficult to measure directly, and only limited guidance and few observed data are available from the literature. Many studies, however, report storm event mean concentrations (EMCs). These EMCs must arise as a result of the buildup and washoff processes, but typically represent the net contribution from a variety of pervious and impervious surfaces. This paper explores the relationship between EMCs and buildup/washoff parameters. An assumption of the mathematical form of the buildup/washoff relationship gives an algebraic expression for the EMC consistent with model assumptions. This yields techniques to separate observed EMCs into contributions from different land uses and from pervious and impervious surfaces. Given this relationship, numerical optimization may be used to estimate site specific values of buildup and washoff parameters from observed storm EMCs for use in modeling. Use of this approach helps ensure that model parameters are consistent with observed data, providing a rational starting point for final model calibration. Several site examples demonstrate use of the method.     

Mapping probability of fire occurrence in San Jacinto Mountains,California, USA

An ecological data base for the San Jacinto Mountains, California, USA, was used to construct a probability model of wildland fire occurrence. The model incorporates both environmental and human factors, including vegetation, temperature, precipitation, human structures, and transportation. Spatial autocorrelation was examined for both fire activity and vegetation to determine the specification of neighborhood effects in the model. Parameters were estimated using stepwise logistic regressions. Among the explanatory variables, the variable that represents the neighborhood effects of spatial processes is shown to be of great importance in the distribution of wildland fires. An important implication of this result is that the management of wildland fires must take into consideration neighborhood effects in addition to environmental and human factors. The distribution of fire occurrence probability is more accurately mapped when the model incorporates the spatial term of neighborhood effects. The map of fire occurrence probability is useful for designing large-scale management strategies of wildfire prevention.     

Effect of the Spatial Variability of Land Use,Soil Type,and Precipitation on Streamflows in Small Watersheds1

Abstract: The spatial variability of the data used in models includes the spatial discretization of the system into subsystems, the data resolution, and the spatial distribution of hydrologic features and parameters. In this study, we investigate the effect of the spatial distribution of land use, soil type, and precipitation on the simulated flows at the outlet of “small watersheds” (i.e., watersheds with times of concentration shorter than the model computational time step). The Soil and Water Assessment Tool model was used to estimate runoff and hydrographs. Different representations of the spatial data resulted in comparable model performances and even the use of uniform land use and soil type maps, instead of spatially distributed, was not noticeable. It was found that, although spatially distributed data help understand the characteristics of the watershed and provide valuable information to distributed hydrologic models, when the watershed is small, realistic representations of the spatial data do not necessarily improve the model performance. The results obtained from this study provide insights on the relevance of taking into account the spatial distribution of land use, soil type, and precipitation when modeling small watersheds.     

Sensitivity of Thermal Habitat of a Trout Stream to Potential Climate Change,Wisconsin, United States1

Abstract: Airborne thermal remote sensing from four flights on a single day from a single‐engine airplane was used to collect thermal infrared data of a 10.47‐km reach of the upper East Branch Pecatonica River in southwest Wisconsin. The study uses a one‐dimensional stream temperature model calibrated with the longitudinal profiles of stream temperature created from the four thermal imaging flights and validated with three days of continuous stream temperature data from instream data loggers on the days surrounding the thermal remote‐sensing campaign. Model simulations were used to quantify the sensitivity of stream thermal habitat to increases in air and groundwater temperature and changes in base flow. The simulations indicate that stream temperatures may reach critical maximum thresholds for brook trout (Salvelinus fontinalis) and brown trout (Salmo trutta) mortality, particularly if both air temperature increases and base flow declines. The approach demonstrates that thermal infrared data can greatly assist stream temperature model validation due to its high spatial resolution, and that this spatially continuous stream temperature data can be used to pinpoint spatial heterogeneity in groundwater inflow to streams. With this spatially distributed data on thermal heterogeneity and base‐flow accretion, stream temperature models considering various climate change scenarios are able to identify thermal refugia that will be critical for fisheries management under a changing climate.     

FACTORS AFFECTING SALINITY REDUCTION IN LAKE TARPON,PINELLAS COUNTY,FLORIDA1

ABSTRACT: Following an enclosure of a sink-hole connecting Lake Tarpon to the Gulf of Mexico, the chloride concentration of lake waters decreased. Water and chloride budgets for the lake in 1975 were prepared, and predictions using the model of Lerman and Brunskill (1971) were made as to the time required for the lake to achieve fresh water status. Model verification indicated good agreement with predictions in 1976; however, data on [C1^-] for 1977 and 1978 are not as supportive of the model used. The information concerning the Lake Tarpon watershed provided by this latter fact is discussed.     

Empirical relationship between use, area, and ambient air concentration of methyl bromide

Methyl bromide (MeBr) is one of the most widely used soil fumigants. Human exposure to MeBr above threshold values can cause serious health problems. The exposure assessment of MeBr depends on estimation or measurement of its air concentrations. This study proposed a methodology for systematically exploring the empirical relationship between MeBr use intensity and ambient air concentrations. Monitored air concentrations were regressed to MeBr use over various spatiotemporal scales that step-wise increased around the monitoring site and monitoring period. The results showed that the goodness-of-fit varied with the spatiotemporal scale of MeBr use. The best fit was Y = 0.46 + 0.00120X (R2 = 0.95, n = 11), where Y was the 8-wk average ambient air concentration (microg/m3), and X was the weekly average use (kg/wk) over an area of 11.3 x 11.3 km (7 x 7 mi). The model was calibrated with air-monitoring data and use data of 2000, and verified with the same type data of 2001. The model estimated subchronic air concentration with reasonable accuracy.     

Enhancing perspectives on lake impairments using satellite observations: A case study on High Rock Lake,North Carolina

Stakeholders developing water quality improvement plans for lakes and reservoirs are challenged by the sparsity of in-situ data and the uncertainty ingrained in management decisions. This study explores how satellite images can fill gaps in water quality databases and provide more holistic assessments of impairments. The study site is an impaired water body that is serving as a pilot for improving state-wide nutrient management planning processes. An existing in-situ database was used to calibrate semi-analytical models that relate satellite reflectance values to turbidity and total suspended solids (TSS). Landsat-7 images from 1999 to 2020 that overpass High Rock Lake, North Carolina were downloaded and processed, providing 42 turbidity and 39 TSS satellite and in-situ match-ups for model calibration and validation. Model r-squared values for the fitted turbidity and TSS models are 0.72 and 0.74, and the mean absolute errors are 14.6 NTU and 3.2 mg/L. The satellite estimates were compared to the in-situ data and simulated TSS values produced by a calibrated hydrologic-hydrodynamic model. The process-based model is considered less accurate than the satellite model based on statistical performance metrics. Comparisons between data sources are illustrated with time series plots, frequency curves, and aggregate decision metrics to highlight the dependence of lake impairment assessments on the spatial and temporal frequency of available data and model accuracy.     

Valuation of National Park System Visitation: The Efficient Use of Count Data Models, Meta-Analysis, and Secondary Visitor Survey Data

The National Park Service (NPS) currently manages a large and diverse system of park units nationwide which received an estimated 279 million recreational visits in 2011. This article uses park visitor data collected by the NPS Visitor Services Project to estimate a consistent set of count data travel cost models of park visitor willingness to pay (WTP). Models were estimated using 58 different park unit survey datasets. WTP estimates for these 58 park surveys were used within a meta-regression analysis model to predict average and total WTP for NPS recreational visitation system-wide. Estimated WTP per NPS visit in 2011 averaged $102 system-wide, and ranged across park units from $67 to $288. Total 2011 visitor WTP for the NPS system is estimated at $28.5 billion with a 95% confidence interval of $19.7–$43.1 billion. The estimation of a meta-regression model using consistently collected data and identical specification of visitor WTP models greatly reduces problems common to meta-regression models, including sample selection bias, primary data heterogeneity, and heteroskedasticity, as well as some aspects of panel effects. The article provides the first estimate of total annual NPS visitor WTP within the literature directly based on NPS visitor survey data.     

INTEGRATING A GEOGRAPHIC INFORMATION SYSTEM,A SCIENTIFIC VISUALIZATION SYSTEM,AND A PRECIPITATION MODEL1

ABSTRACT: Investigating natural, potential, and human-induced impacts on hydrologic systems commonly requires complex modeling with overlapping data requirements, plus massive amounts of one- to four-dimensional data at multiple scales and formats. Given the complexity of most hydrologic studies, the requisite software infrastructure must incorporate many components including simulation modeling and spatial analysis with a flexible, intuitive display. Integrating geographic information systems (GIS) and scientific visualization systems (SVS) provides such an infrastructure. This paper describes an integrated system consisting of an orographic precipitation model, a GIS, and an SVS. The results of this study provide a basis for improving the understanding of hydro-climatic processes in mountainous regions. An additional benefit of the integrated system, the value of which is often underestimated, is the improved ability to communicate model results, leading to a broader understanding of the model assumptions, sensitivities, and conclusions at a management level.     

Using Multiple Watershed Models to Predict Water,Nitrogen, and Phosphorus Discharges to the Patuxent Estuary1

Boomer, Kathleen M.B., Donald E. Weller, Thomas E. Jordan, Lewis Linker, Zhi‐Jun Liu, James Reilly, Gary Shenk, and Alexey A. Voinov, 2012. Using Multiple Watershed Models to Predict Water, Nitrogen, and Phosphorus Discharges to the Patuxent Estuary. Journal of the American Water Resources Association (JAWRA) 1‐25. DOI: 10.1111/j.1752‐1688.2012.00689.x Abstract: We analyzed an ensemble of watershed models that predict flow, nitrogen, and phosphorus discharges. The models differed in scope and complexity and used different input data, but all had been applied to evaluate human impacts on discharges to the Patuxent River or to the Chesapeake Bay. We compared predictions to observations of average annual, annual time series, and monthly discharge leaving three basins. No model consistently matched observed discharges better than the others, and predictions differed as much as 150% for every basin. Models that agreed best with the observations in one basin often were among the worst models for another material or basin. Combining model predictions into a model average improved overall reliability in matching observations, and the range of predictions helped describe uncertainty. The model average was not the closest to the observed discharge for every material, basin, and time frame, but the model average had the highest Nash–Sutcliffe performance across all combinations. Consistently poor performance in predicting phosphorus loads suggests that none of the models capture major controls. Differences among model predictions came from differences in model structures, input data, and the time period considered, and also to errors in the observed discharge. Ensemble watershed modeling helped identify research needs and quantify the uncertainties that should be considered when using the models in management decisions.     

USE OF THE KRIGING METHOD FOR STUDYING CHARACTERISTICS OF GROUND WATER DROUGHTS1

round water drought events were derived by taking a truncation level through the time series of daily ground water depth that are recorded elevation differences between the water table and land surface at a well site. Droughts of various truncation levels at 70, 80, 90, and 95 percent, were obtained, where a 70 percent truncation level means that 70 percent of ground water depth data are less than or equal to the truncated value. The conditional probability that a drought occurring at a certain truncation level will prolong and advance to that of the next higher level was estimated. The regionalization analysis was conducted assuming that conditional probabilities estimated at selected wells are regionalized variables. Contour lines of conditional probabilities for each truncation level were constructed to express their spatial variability in the region. Estimation errors associated with the regionalization were reasonably small.